Analyzing Microseismic Data from a Fracture Treatment

ABSTRACT

Systems, methods, and software can be used to analyze microseismic data from a fracture treatment. In some aspects, data for a new microseismic event are from a fracture treatment of a subterranean zone. An updated parameter for a fracture plane is calculated. The fracture plane was previously generated based on data for prior microseismic events. The updated parameter calculated is calculated based on the data for the new microseismic event and the data for the prior microseismic events. A graphical representation of the fracture plane is displayed based on the updated parameter.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. Provisional Application Ser.No. 61/710,582, entitled “Identifying Dominant Fracture Orientations,”filed on Oct. 5, 2012.

BACKGROUND

This specification relates to analyzing microseismic data from afracture treatment. Microseismic data are often acquired in associationwith hydraulic fracturing treatments applied to a subterraneanformation. The hydraulic fracturing treatments are typically applied toinduce artificial fractures in the subterranean formation, and tothereby enhance hydrocarbon productivity of the subterranean formation.The pressures generated by the fracture treatment can inducelow-amplitude or low-energy seismic events in the subterraneanformation, and the events can be detected by sensors and collected foranalysis.

SUMMARY

In one general aspect, microseismic data from a fracture treatment areanalyzed. In some instances, the data may be analyzed in real time, forexample, during the fracture treatment.

In some aspects, data for a new microseismic event are collected from afracture treatment of a subterranean zone. An updated parameter for afracture plane is calculated. The fracture plane was previouslygenerated based on data for prior microseismic events. The updatedparameter calculated is calculated based on the data for the newmicroseismic event and the data for the prior available microseismicevents. A graphical representation of the fracture plane (or a numericalrepresentation of the fracture plane parameters) is displayed based onthe updated parameter.

Implementations may include one or more of the following features. Priorknowledge or estimates of possible fracture planes orientations is usedto calculate a fracture plane parameter. The graphical representationare continuously updated, for example, as long as additional newmicroseismic events appear in the system input buffer. New microseismicevents are collected from the fracture treatment before the fracturetreatment begins, during the fracture treatment, after the fracturetreatment has terminated, or any combination of these. The updatedparameter is calculated and the graphical representation is displayed inreal time during the fracture treatment. The fracture plane is selectedfrom multiple fracture planes based on the data for the new microseismicevent. The new microseismic event is associated with the selectedfracture plane. Displaying a graphical representation of the fractureplane includes updating a graphical representation of the fractureplanes in real time during the fracture treatment. Selecting thefracture plane from the fracture planes includes identifying a distancebetween the new microseismic event and the selected facture plane anddetermining that the distance is less than a threshold distance. Thethreshold value is a static predefined value. The predefined thresholdis computed by multiplying a coefficient and the standard deviation oruncertainty of the fracture plane. The coefficient can be a predefinedconstant value, for example, between 1 and 2, or another value.

Additionally or alternatively, these and other implementations mayinclude one or more of the following features. Calculating an updatedparameter for the fracture plane includes calculating at least one of anupdated orientation or an updated area for the fracture plane based onthe data for the new microseismic event and the data for the priormicroseismic events. Calculating an updated parameter for the fractureplane includes calculating an average distance from the fracture planefor the new microseismic event and the prior microseismic events. Thenew microseismic event and the prior microseismic events define a set.In response to detecting that the average distance is greater than apredefined threshold distance, an updated average distance is calculatedafter removing one or more microseismic events from the set.

Additionally or alternatively, these and other implementations mayinclude one or more of the following features. Calculating an updatedparameter for the fracture plane includes calculating an updated areafor the fracture plane. The updated area for the fracture plane iscompared to a prior area for the fracture plane. The new microseismicevent is disassociated from the fracture plane if the updated area forthe fracture plane is less than the prior area for the fracture plane.The new microseismic event is a first new microseismic event. Afterdisplaying the graphical representation based on the first newmicroseismic event, data for a second new microseismic event collectedfrom the fracture treatment is received. A second updated parameter iscalculated for the fracture plane based in part on the data for thesecond new microseismic event. An updated graphical representation ofthe fracture plane is generated based on the second updated parameter.

The details of one or more implementations are set forth in theaccompanying drawings and the description below. Other features,objects, and advantages will be apparent from the description anddrawings, and from the claims.

DESCRIPTION OF DRAWINGS

FIG. 1A is a diagram of an example well system; FIG. 1B is a diagram ofthe example computing subsystem 110 of FIG. 1A.

FIGS. 2A and 2B are plots showing example fracture planes.

FIGS. 3A-3F are plots showing updates for an example fracture plane.

FIG. 4 is a flow chart of an example technique for analyzingmicroseismic data.

Like reference symbols in the various drawings indicate like elements.

DETAILED DESCRIPTION

In some aspects of what is described here, fracture parameters, dominantfracture orientations, or other data are identified from microseismicdata. In some instances, these or other types of data are dynamicallyidentified, for example, in a real-time fashion during a fracturetreatment. For many applications and analysis techniques, anidentification of fracture planes from real-time microseismic events isneeded, and individual fracture planes can be displayed to show timeevolution and geometric elimination, including location, propagation,growth, reduction, or elimination of the fracture planes. Suchcapabilities can be incorporated into control systems, software,hardware, or other types of tools available to oil and gas fieldengineers when they analyze potential oil and gas fields, whilestimulating hydraulic fractures and analyzing the resultant signals.Such tools can provide a reliable and direct interface for presentingand visualizing the dynamics of hydraulic fractures, which may assist inanalyzing the fracture complexity, fracture network structure, andreservoir geometry. Such tools can assist in evaluating theeffectiveness of hydraulic fracturing treatment, for example, byimproving, enhancing, or optimizing the fracture density and tracelengths and heights. Such improvements in the fracture treatment appliedto the reservoir may enhance production of hydrocarbons or otherresources from the reservoir.

Hydraulic fracture treatments can be applied in any suitablesubterranean zone. Hydraulic fracture treatments are often applied intight formations with low-permeability reservoirs, which may include,for example, low-permeability conventional oil and gas reservoirs,continuous basin-centered resource plays and shale gas reservoirs, orother types of formations. Hydraulic fracturing can induce artificialfractures in the subsurface, which can enhance the hydrocarbonproductivity of a reservoir.

During the application of a hydraulic fracture treatment, the injectionof high-pressure fluids can alter stresses, accumulate shear stresses,and cause other effects within the geological subsurface structures. Insome instances, microseismic events are associated with hydraulicfractures induced by the fracturing activities. The acoustic energy orsounds associated with rock stresses, deformations, and fracturing canbe detected and collected by sensors. In some instances, microseismicevents have low-energy (e.g., with the value of the log of the intensityor moment magnitude of less than three), and some uncertainty oraccuracy or measurement error is associated with the event locations.The uncertainty can be described, for example, by a prolate spheroid,where the highest likelihood is at the spheroid center and the lowestlikelihood is at the edge.

Microseismic event mapping can be used to geometrically locate thesource point of the microseismic events based on the detectedcompressional and shear waves. The detected compressional and shearwaves (e.g., p-waves and s-waves) can yield additional information aboutmicroseismic events, including the location of the source point, theevent's location and position measurement uncertainty, the event'soccurrence time, the event's moment magnitude, the direction of particlemotion and energy emission spectrum, and possibly others. Themicroseismic events can be monitored in real time, and in someinstances, the events are also processed in real time during thefracture treatment. In some instances, after the fracture treatment, themicroseismic events collected from the treatment are processed togetheras “post data.”

Processing microseismic event data collected from a fracture treatmentcan include fracture matching (also called fracture mapping). Fracturematching processes can identify fracture planes in any zone based onmicroseismic events collected from the zone. Some example computationalalgorithms for fracture matching utilize microseismic event data (e.g.,an event's location, an event's location measurement uncertainty, anevent's moment magnitude, etc.) to identify individual fractures thatmatch the collected set of microseismic events. Some examplecomputational algorithms can compute statistical properties of fracturepatterns. The statistical properties may include, for example, fractureorientation, fracture orientation trends, fracture size (e.g., length,height, area, etc.), fracture density, fracture complexity, fracturenetwork properties, etc. Some computational algorithms account foruncertainty in the events' location by using multiple realizations ofthe microseismic event locations. For example, alternative statisticalrealizations associated with Monte Carlo techniques can be used for adefined probability distribution on a spheroid or another type ofdistribution.

Generally, fracture matching algorithms can operate on real-time data,post data, or any suitable combination of these and other types of data.Some computational algorithms for fracture matching operate only on postdata. Algorithms operating on post data can be used when any subset orseveral subsets of microseismic data to be processed has been collectedfrom the fracture treatment; such algorithms can access (e.g., as aninitial input) the full subset of microseismic events to be processed.In some implementations, fracture matching algorithms can operate onreal-time data. Such algorithms may be used for real-time automaticfracture matching during the fracture treatment. Algorithms operating onreal-time data can be used during the fracture treatment, and suchalgorithms can adapt or dynamically update a previously-identifiedfracture model to reflect newly-acquired microseismic events. Forexample, once a microseismic event is detected and collected from thetreatment field, a real-time automatic fracture matching algorithm mayrespond to this new event by dynamically identifying and extractingfracture planes from the already-collected microseismic events in areal-time fashion. Some computational algorithms for fracture matchingcan operate on a combination of post data and real-time data.

In some cases, fracture mapping algorithms are configured to handleconditions that arise in real-time microseismic data processing. Forexample, several types of challenges or conditions may occur morepredominantly in the real-time context. In some instances, real-timeprocessing techniques can be adapted to account for (or to reduce oravoid) the lower accuracy that is sometimes associated with fracturesextracted from data sets lacking a sufficient number of microseismicevents or lacking a sufficient number of microseismic events in certainparts of the domain. Some real-time processing techniques can be adaptedto produce fracture data that are consistent with the fracture dataobtainable from post data processing techniques. For example, some ofthe example real-time processing techniques described here have producedresults that are statistically the same, according to the statisticalhypothesis test (t test and F test), as results produced by post dataprocessing techniques on the same data.

In some cases, real-time processing techniques can be adapted to readily(e.g., instantaneously, from a user's perspective) offer the identifiedfracture data to users. Such features may allow field engineers oroperators to dynamically obtain fracture geometric information andadjust fracture treatment parameters when appropriate (e.g. to improve,enhance, optimize, or otherwise change the treatment). In someinstances, fracture planes are dynamically extracted from microseismicdata and displayed to field engineers in real time. Real-time processingtechniques can exhibit high-speed performance. In some cases, theperformance can be enhanced by parallel computing technology,distributed computing technology, parallel threading approaches, fastbinary-search algorithms, or a combination of these and other hardwareand software solutions that facilitate the real-time operations.

In some implementations, fracture matching technology can directlypresent information about fractures planes associated withthree-dimensional microseismic events. The fracture planes presented canrepresent fracture networks that exhibit multiple orientations andactivate complex fracture patterns. In some cases, hydraulic fractureparameters are extracted from a cloud of microseismic event data; suchparameters may include, for example, fracture orientation trends,fracture density and fracture complexity. The fracture parameterinformation can be presented to field engineers or operators, forexample, in a tabular, numerical, or graphical interface or an interfacethat combines tabular, numerical, and graphical elements. The graphicalinterface can be presented in real time and can exhibit the real-timedynamics of hydraulic fractures. In some instances, this can help fieldengineers analyze the fracture complexity, the fracture network andreservoir geometry, or it can help them better understand the hydraulicfracturing process as it progresses.

In some implementations, accuracy confidence values are used to quantifythe certainty of the fracture planes extracted from microseismic data.The accuracy confidence values can be used to classify the fracturesinto confidence levels. For example, three confidence levels (lowconfidence level, medium confidence level and high confidence level) areappropriate for some contexts, while in other contexts a differentnumber (e.g., two, four, five, etc.) of confidence levels may beappropriate. A fracture plane's accuracy confidence value can becalculated based on any appropriate data. In some implementations, afracture plane's accuracy confidence value is calculated based on themicroseismic events' locations and position uncertainties, individualmicroseismic events' moment magnitude, distances between individualevents and their supporting fracture plane, the number of supportingevents associated with the fracture plane, and the weight of variationof the fracture orientation, among others.

The accuracy confidence values can be computed and the fracture planescan be classified at any appropriate time. In some cases, the accuracyconfidence values are computed and the fracture planes are classified inreal time during the fracture treatment. The fracture planes can bepresented to the user at any appropriate time and in any suitableformat. In some instances, the fracture planes are presented graphicallyin a user interface in real time according to the accuracy confidencevalues, according to the accuracy confidence levels, or according to anyother type of classification. In some instances, users can selectindividual groups or individual planes (e.g., those with high confidencelevels) for viewing or analysis. The fracture planes can be presented tothe user in an algebraic format, a numerical format, graphical format,or a combination of these and other formats.

In some implementations, microseismic events are monitored in real timeduring the hydraulic fracture treatment. As the events are monitored,they may also be processed in real time, they may be processed later aspost data, or they may be processed using a combination of real time andpost data processing. The events may be processed by any suitabletechnique. In some cases, the events are processed individually, at thetime and in the order in which they are received. For example, a systemstate S(M, N−1) can be used to represent the M number of planesgenerated from the N−1 previous events. The new incoming N^(th) eventcan trigger the system S(M, N−1). In some cases, upon receiving the theN^(th) event, a histogram or distribution of orientation ranges isgenerated. For example, a probability distribution histogram or theHough transform histogram of the degenerated planes in the strike anddip angle domain can be generated to identify the feasible dominantorientations imbedded in the fractures sets.

A basic plane can be generated from a subset of microseismic events. Forexample, any three non-collinear points in space mathematically define abasic plane. The basic plane defined by three non-collinear microseismicevents can be represented by the normal vector (a, b, c). The normalvector (a, b, c) may be computed based on the three events' positions.The basic plane's orientation can be computed from the normal vector.For example, the dip θ and the strike φ can be given by

$\begin{matrix}{{\theta = {\arctan \; \frac{\sqrt{a^{2} + b^{2}}}{2}}},{\phi = {\arctan \; {\frac{b}{a}.}}}} & (1)\end{matrix}$

The dip angle θ of a fracture plane can represent the angle between thefracture plane and the horizontal plane (e.g., the xy-plane). The strikeangle φ of a fracture plane can represent the angle between a horizontalreference axis (e.g., the x-axis) and a horizontal line where thefracture plane intersects the horizontal plane. For example, the strikeangle can be defined with respect to North or another horizontalreference direction. A fracture plane can be defined by otherparameters, including angular parameters other than the strike angle anddip angle.

In general, N events can support P basic planes, where P=N(N−1)(N−2)/6,strike and dip angles. A probability histogram can be constructed fromthe orientation angles. The probability histogram or the enhanced Houghtransformation histogram can have any suitable configuration. Forexample, the histogram configuration can be based on a fixed bin sizeand a fixed number of bins, natural optimal bin size in the strike anddip angle domain, or other types of bins. The histogram can be based onany suitable number of microseismic events (e.g., tens, hundreds,thousands, etc.), and any suitable range of orientations. In some cases,multiple discrete bins are defined for the histogram, and each binrepresents a discrete range of orientations. A quantity of basic planesin each discrete range can be computed from the basic planes. In somecases, each basic plane's orientation falls within the orientation rangeassociated with one of the bins. For example, for N microseismic events,each of the P basic planes can be assigned to a bin, and the quantity ofbasic planes assigned to each bin can be computed. The quantity computedfor each bin can be any suitable value. For example, the quantity can bea non-normalized number of basic planes, the quantity can be anormalized probability, frequency, or fraction of basic planes, or thequantity can be another type of value that is suitable for a histogram.A histogram can be generated to represent the quantity of basic planesassigned to all of the bins, or to represent the quantity of basicplanes assigned to a subset of the bins.

In some examples, the histogram is presented as a three-dimensional barchart, a three-dimensional surface map, or another suitable plot in anappropriate coordinate system. The peaks on the histogram plot canindicate dominant fracture orientations. For example, along one axis thehistogram may represent strike angles from 0° through 360° (or anotherrange), and the strike angles can be divided into any suitable number ofbins; along another axis the histogram may represent dip angles from 60°through 90° (or another range), and the dip angles can be divided intoany suitable number of bins. The quantity (e.g., probability) for eachbin can be represented along a third axis in the histogram. Theresulting plot can exhibit local maxima (peaks). Each local maximum(peak) can indicate a respective strike angle and dip angle thatrepresents a dominant fracture orientation. For example, the localmaximum of the histogram may indicate that more basic planes are alignedalong this direction (or range of directions) than along neighboringdirections, and these basic planes are either closely parallel orsubstantially on the same plane.

The orientation range represented by each bin in the histogram can bedetermined by any appropriate technique. In some cases, each binrepresents a pre-determined range of orientations. For example, thefixed bin size method can be used. In some cases, the range or size foreach bin is computed based on the data to be represented by thehistogram. For example, the natural optimal bin size method can be used.In some instances, the basic plane orientations are sorted, and clustersof sorted orientations are identified. For example, all strikes can besorted in a decreasing or increasing order and then grouped intoclusters; similarly, all dip values can be sorted in a decreasing orincreasing order and then grouped into clusters. The clusters can beassociated with two-dimensional grid, and the number of basic planes ineach grid cell can be counted. In some cases, this technique cangenerate adaptive and dynamic clusters, leading to highly accuratevalues for the dominant orientations. This technique and associatedrefinements can be implemented with N³log(N) computational complexity.In some cases, the bin sizes for both the strike and dip are fixed, andeach basic plane's location grid cell can be explicitly determined bythe associated strike and dip with N³ computational complexity.

Fracture planes associated with a set of microseismic events can beextracted from the dominant orientations embedded in the histogram data.Basic planes that support the dominant orientation (θ, φ) may be eithernearly parallel or on the same plane. Basic planes located within thesame plane can be merged together, forming a new fracture plane withstronger support (e.g., representing a larger number of microseismicevents). Any suitable technique can be used to merge the fractureplanes. In some cases, for each dominant orientation (θ, φ), a normal tothe plane vector is constructed with components (sin θ cos φ, sin θ sinφ, cos θ). In some instances, the results are insensitive to thelocation of the plane, and without loss of generality, the plane can beconstructed from this normal vector (e.g., assuming the origin is in theplane). The plane can be described by x sin θ cos φ+y sin θ sin φ+z cosθ=0. The normal signed distance of each event (x_(o), y_(o), z_(o)) froma basic plane to the constructed plane can be represented d=−(x₀ sin θcos φ+y₀ sin θ sin φ+x₀ cos θ). In this representation, events withopposite signs of d are located opposite sides of the plane.

In some cases, microseismic events are grouped into clusters based ontheir distance from the constructed fracture planes. For example, acluster of events can contain the group of events closest to aconstructed fracture plane. As such, each cluster of microseismic eventscan support a particular fracture plane. The cluster size refers to thenumber of the events the cluster contains. In some cases, user input orother program data can designate a minimum number of events in asustained cluster. The minimum cluster size can depend on the number ofmicroseismic events in the data. In some instances, the minimum clustersize should be larger than or equal three. For example, clusters havinga size larger than or equal to the minimum cluster size can beconsidered legitimate fracture planes. A fitting algorithm can beapplied to the location and location uncertainty values for the eventsin each cluster to find their corresponding fracture plane.

Any suitable technique can be used to identify a fracture plane from aset of microseismic events. In some cases, a Chi-square fittingtechnique is used. Given K observed microseismic events, the locationscan be represented (x_(i), y_(i), z_(i)), and their measurementuncertainties can be represented σ_(i,x), σ_(i,y), σ_(i,z)), where1≦i≦K. The parameters of the plane model z=ax+by+c can be calculated,for example, by minimizing the Chi-square merit function

$\begin{matrix}{{\chi^{2}\left( {a,b,c} \right)} = {\sum\limits_{i = 1}^{K}\frac{\left( {z_{i} - {ax}_{i} - {by}_{i} - c} \right)^{2}}{\sigma_{i,z}^{2} + {a^{2}\sigma_{i,x}^{2}} + {b^{2}\sigma_{i,y}^{2}}}}} & (2)\end{matrix}$

The Chi-square merit function can be solved by any suitable technique.In some instances, a solution can be obtained by solving threeequations, which are the partial derivatives of χ² (a, b, c) withrespect to its variables, where each partial derivative is forced tozero. In some instances, there is no analytical solution for thisnonlinear mathematical system of equations. Numerical methods (e.g.,Newton's numerical method, the Newton Rafson method, the conjugategradient method, or another technique) can be applied to solve for theparameters a, b and c, and the strike and dip angles can be computed(e.g., using equation (1) above). The orientation of the dominantfracture plane computed from the microseismic events can be the same as,or it can be slightly different from, the dominant fracture orientationidentified from the histogram.

In some implementations, an algorithm iterates over all possibledominant orientations to expand all feasible fracture planes. In somecases, the algorithm iterates over a selected subset of possibledominant orientations. The iterations can converge to planes. Someplanes may be exactly equal to each other and some may be close to eachother. Two planes can be considered “close” to each other, for example,when the average distance of one plane's events from another plane isless than a given threshold. The threshold distance can be designated,for example, as a control parameter. The algorithm can merge closeplanes together and the support events of one plane can be associatedwith the support events of the other merged plane(s).

In some cases, constraints are imposed on the fracture planes identifiedfrom the microseismic data. For example, in some cases, the distanceresidual of events must be less than a given tolerance distance. Thetolerance distance can be designated, for example, as a controlparameter. In some instances, the identified fracture planes need to beproperly truncated to represent the finite size of fractures. Theboundary of truncated planes can be calculated from the support events'position and the events' location measurement uncertainty. The newfinite-size fracture planes can be merged with the already-identifiedfractures.

In some instances, a new incoming N^(th) microseismic event isassociated with the fracture planes already identified based on theprevious N−1 microseismic events. Upon associating the new event with anexisting fracture, an algorithm can be used to update the existingfracture. For example, updating the fracture may change the fracture'sgeometry, location, orientation, or other parameters. Upon choosing oneof the previously-identified fracture planes, the fracture plane'sdistance from the new event can be calculated. If the distance is lessthan or equal to the distance control parameter, the new event can beadded to the supporting event set for the fracture plane. If thedistance is larger than the distance control parameter, otherpreviously-identified fracture planes can be selected (e.g., iterativelyor recursively) until a plane within the threshold distance is found.After the new event is added to a support set for a fracture plane, newstrike and dip values can be evaluated and if needed can bere-calculated (e.g., using the Chi-square fitting method, or anotherstatistical or deterministic technique) for the fracture plane.Typically, re-calculating the fracture parameters causes limited changein the orientation due to the conditional control of the distance.

In some cases, when a new microseismic event is associated with afracture plane, one or more parameters (e.g., distance residual, area,etc.) can be modified or optimized. The plane's distance residual r canrepresent the average distance from the supporting events to the plane.If the distance residual is less than the given residual tolerance T,the new event can be flagged to the associated events set for the plane.In some cases, an additional process, via which other associated eventsof the supporting set are taken-off the list, is launched and isterminated when the distance residual r falls within the given T. Afracture plane's area can represent the size of the fracture plane.Experience shows that usually a new event causes the fracture plane topropagate in length, grow in height, or both. Thus computationalprocesses can be constrained by a non-decreasing area condition, wherebythe new plane's area should grow larger than or remain equal to that ofthe original plane (rather than shrink) when the new event is added tothe plane.

A fracture plane's orientation can represent the angle of the fractureplane. For example, a normal vector, the strike and dip angles, or othersuitable parameters can be used to represent the fracture planeorientation. A change in a fracture plane's orientation (or otherchanges to a fracture plane) can cause some associated support events tobe removed out of the associated events list to the un-associated eventlist based on their distance from the updated fracture plane.Additionally or alternatively, a change in a fracture plane'sorientation can cause some previously-unassociated events to be assignedto the fracture plane based on their proximity to the updated fractureplane. Additionally, some events associated with nearby planes may alsobe associated with the current plane. If a new event is associated totwo fracture planes, the fracture planes may intersect each other. Insome cases, intersecting planes can be merged. If the new event does notbelong to any existing fracture plane, it can be assigned to the“unassociated events” list.

The accumulated N microseismic events can be considered at any point tobe a subset of the final post data event set. In such cases, thehistogram or distribution of orientations based on the first N eventsmay be different from the histogram or distribution of orientationsconstructed from the final post data. Some fracture planes extractedfrom N microseismic events may not be accurate, and this inaccuracy candecrease as time increases and more events are accumulated. As anexample, accuracy and confidence may be lower at an initial time whenthe detected fracture planes are associated with microseismic eventslocated close to the well bore. Such data may indicate fracture planesthat are nearly parallel to the wellbore, even if those planes do notrepresent real fractures.

Fracture accuracy confidence can be used a measure for the certaintyassociated with fracture planes identified from microseismic data. Insome cases, the accuracy confidence is identified in real time duringthe fracture treatment. The accuracy confidence can be determined fromany suitable data using any suitable calculations. In some cases, theaccuracy confidence value for a fracture plane is influenced by thenumber of microseismic events associated with the fracture plane. Forexample, the accuracy confidence value can scale (e.g., linearly,non-linearly, exponentially, polynomially, etc.) with the number ofmicroseismic events according to a function. The number of microseismicevents associated with a fracture plane can be incorporated (e.g., as aweight, an exponent, etc.) in an equation for calculating the accuracyconfidence. In some instances, a fracture plane has a higher confidencevalue when the fracture plane is supported by a larger number ofmicroseismic data points (or a lower confidence value when the fractureplane is supported by a smaller number of microseismic data points).

In some cases, the accuracy confidence value for a fracture plane isinfluenced by the location uncertainty for the microseismic eventsassociated with the fracture plane. For example, the accuracy confidencevalue can scale (e.g., linearly, non-linearly, exponentially,polynomially, etc.) with the microseismic event's location uncertaintyaccording to a function. The microseismic event's location uncertaintycan be incorporated (e.g., as a weight, an exponent, or any decayingfunction of the distance, etc.) in an equation for calculating theaccuracy confidence. In some instances, a fracture plane has a higherconfidence value when the fracture plane is supported by microseismicdata points having lower uncertainty (or a lower confidence value whenthe fracture plane is supported by microseismic data points havinghigher uncertainty).

In some cases, the accuracy confidence value for a fracture plane isinfluenced by the moment magnitude for the microseismic eventsassociated with the fracture plane. For example, the accuracy confidencevalue can scale (e.g., linearly, non-linearly, exponentially,polynomially, etc.) with the microseismic event's moment magnitudeaccording to a function. The microseismic event's moment magnitude canbe incorporated (e.g., as a weight, an exponent, etc.) in an equationfor calculating the accuracy confidence. The moment magnitude for amicroseismic event can refer to the energy or intensity (sometimesproportional to the square of the amplitude) of the event. For example,the moment magnitude for a microseismic event can be a logarithmic scalevalue of the energy or intensity, or another type of value representingenergy intensity. In some instances, a fracture plane has a higherconfidence value when the fracture plane is supported by microseismicdata points having higher intensity (or a lower confidence value whenthe fracture plane is supported by microseismic data points having lowerintensity).

In some cases, the accuracy confidence value for a fracture plane isinfluenced by the distance between the fracture plane and themicroseismic events associated with the fracture plane. For example, theaccuracy confidence value can scale (e.g., linearly, non-linearly,exponentially, polynomially, etc.) with the average distance between thefracture plane and the microseismic events supporting the fractureplane. The average distance can be incorporated (e.g., as a weight, anexponent, etc.) in an equation for calculating the accuracy confidence.In some instances, a fracture plane has a higher confidence value whenthe fracture plane is supported by microseismic data points that are, onaverage, closer to the fracture plane (or a lower confidence value whenthe fracture plane is supported by microseismic data points that are, onaverage, farther from the fracture plane).

In some cases, the accuracy confidence value for a fracture plane isinfluenced by the fracture plane's orientation with respect to adominant orientation trend in the microseismic data set. For example,the accuracy confidence value can scale (e.g., linearly, non-linearly,exponentially, polynomially, etc.) with the angular difference betweenthe fracture plane's orientation and a dominant orientation trend in themicroseismic data. The orientation angles can include strike, dip or anyrelevant combination (e.g., a three-dimensional spatial angle). Theorientation can be incorporated (e.g., as a weight, an exponent, etc.)in an equation for calculating the accuracy confidence. A microseismicdata set can have one dominant orientation trend or it can have multipledominant orientation trends. Dominant orientation trends can beclassified, for example, as primary, secondary, etc. In some instances,a fracture plane has a higher confidence value when the fracture planeis aligned with a dominant orientation trend in the microseismic dataset (or a lower confidence value when the fracture plane is deviatedfrom the dominant orientation trend in the microseismic data set).

A weighting value called the “weight of variation of fractureorientation” can represent the angular difference between the fractureplane's orientation and a dominant orientation trend in the microseismicdata. The weight of variation of fracture orientation can be a scalarvalue that is a maximum when the fracture plane is aligned with adominant orientation trend. The weight of variation of fractureorientation can be a minimum for fracture orientations that aremaximally separated from a dominant fracture orientation trend. Forexample, when there is a single dominant fracture orientation trend, theweight of variation of fracture orientation can be zero for fracturesthat are perpendicular (or normal) to the dominant fracture orientation.As another example, when there are multiple dominant fractureorientation trends, the weight of variation of fracture orientation canbe zero for fractures having orientations between the dominant fractureorientations. The weight of variation of the fracture orientation can bethe ratio of the calculated plane's orientation and the orientationreflected by the homogeneous case.

In some cases, when there are multiple dominant fracture orientationtrends, the weight of variation of fracture orientation has the samemaximum value for each dominant fracture orientation trend. In somecases, when there are multiple dominant fracture orientations, theweight of variation of fracture orientation has a different localmaximum value for each dominant fracture orientation. For example, theweight of variation of fracture orientation can be 1.0 for fracturesthat are parallel to a first dominant fracture orientation trend, 0.8for fractures that are parallel to a second dominant fractureorientation trend, and 0.7 for fractures that are parallel to a thirddominant fracture orientation trend. The weight of variation of fractureorientation can decrease to local minima between the dominant fractureorientations trend. For example, the weight of variation of fractureorientation between each neighboring pair of dominant fractureorientations can define a local minimum half way between the dominantfracture orientations or at another point between the dominant fractureorientations.

The accuracy confidence parameter can be influenced by the supportingmicroseismic events' location uncertainty, the supporting microseismicevents' moment magnitude, distance between the supporting microseismicevents and the fracture plane, the number of supporting eventsassociated with the plane, the weight of variation of fractureorientation, other values, or any appropriate combination of one or moreof these. In some general models, the confidence increases as momentmagnitude is larger, and as the variation of the fraction orientationbecomes larger, and the number of supporting events is larger, and theiraccuracy in their location is larger, and as the variation of the weightas a function of the distance is larger. These factors can be used asinputs for defining weight in an equation for the accuracy confidence.For example, in some models, the weights are linear or nonlinearfunctions of these factors and the weight of variation of the fractureorientation may appear with higher weight when influencing the plane'sconfidence. In some examples, the accuracy confidence is calculated as:

$\begin{matrix}{{{Confidence} = \left( {{weight}\mspace{14mu} {of}\mspace{14mu} {variation}\mspace{14mu} {of}\mspace{14mu} {fracture}\mspace{14mu} {orientation}} \right)^{*}}{\sum_{i = 1}^{{number}\mspace{14mu} {of}\mspace{14mu} {events}}{\begin{pmatrix}\begin{matrix}\left( {{location}\mspace{14mu} {uncertainty}\mspace{14mu} {weight}} \right)^{*} \\\left( {{moment}\mspace{14mu} {magnitude}\mspace{14mu} {weight}} \right)^{*}\end{matrix} \\\left( {{distance}\mspace{14mu} {variation}\mspace{14mu} {weight}} \right)\end{pmatrix}.}}} & (3)\end{matrix}$

Other equations or algorithms can be used to compute the confidence.

The identified fracture planes can be classified into confidence levelsbased on the fracture planes' accuracy confidence values. In someinstances, three levels are used: low confidence level, mediumconfidence level and high confidence level. Any suitable number ofconfidence levels can be used. In some examples, when a new event isadded to the supporting set associated with an existing fracture plane,its associated fracture confidence parameter may increase, which maycause the fracture plane to roll from its current confidence level to ahigher one, if it exists. As another example, if a fracture'sorientation diverts away from orientation trends exhibited by postmicroseismic event data, as microseismic events gradually accumulate, adecrease in fracture confidence may be induced, mainly by the weight ofvariation of fracture orientation, causing the plane to decrease itslevel to a lower confidence level, if it exists. This may particularlyapply to fractures created at the initial time of hydraulic fracturingtreatment; it may also apply to other types of fractures in othercontexts.

Users (e.g., field engineers, operational engineers and analysts, andothers) can be provided a graphical display of the fracture planesidentified from the microseismic data. In some cases, the graphicaldisplay allows the user to visualize the identified planes in a realtime fashion, in graphical panels presenting the confidence levels. Forexample, three graphical panels can be used to separately present thelow confidence level, medium confidence level and high confidence levelfracture planes. In some cases, the lower confidence level fractureplanes are created in the initial times of the fracturing treatment. Insome cases, higher confidence level fracture planes propagate in time inthe direction nearly perpendicular to the wellbore. As new microseismicevents gradually accumulate in time, the graphical display can beupdated to enable users to dynamically observe the fracture planesassociation among confidence levels associated with the graphicalpanels.

The confidence level groups can be presented as plots of the fractureplanes, or the confidence level groups can be presented in anotherformat. The confidence level groups can be presented algebraically, forexample, by showing the algebraic parameters (e.g., parameters for theequation of a plane) of the fracture planes in each group. Theconfidence level groups can be presented numerically, for example, byshowing the numerical parameters (e.g., strike, dip, area, etc.) of thefracture planes in each group. The confidence level groups can bepresented in a tabular form, for example, by presenting a table of thealgebraic parameters or numerical parameters of the fracture planes ineach group. Moreover, a fracture plane can be represented graphically ina three-dimensional space, a two-dimensional space, or another space.For example, a fracture plane can be represented in a rectilinearcoordinate system (e.g., x, y, z coordinates) in a polar coordinatesystem (e.g., r, θ, φ coordinates), or another coordinate system. Insome examples, a fracture plane can be represented as a line at thefracture plane's intersection with another plane (e.g., a line in thexy-plane, a line in the xz-plane, a line in the yz-plane, or a line inany arbitrary plane or surface).

In some instances, a graphical display allows users to track andvisualize spatial and temporal evolution of specific fracture planes,including their generation, propagation and growth. For example, a usermay observe stages of a specific fracture plane's spatial and temporalevolution such as, for example, initially identifying the fracture planebased on three microseismic events, a new event that changes the plane'sorientation, a new event that causes the planes' area to grow (e.g.,vertically, horizontally, or both), or other stages in the evolution ofa fracture plane. The spatial and temporal evolution of fracture planesmay present the travel paths of stimulated fluids and proppants injectedinto the rock matrix. Visualization of dynamics of fracture planes canhelp users better understand the hydraulic fracturing process, analyzethe fracture complexity more accurately, evaluate the effectiveness ofhydraulic fracture, or improve the well performance.

Although this application describes examples involving microseismicevent data, the techniques and systems described in this application canbe applied to other types of data. For example, the techniques andsystems described here can be used to process data sets that includedata elements that are unrelated to microseismic events, which mayinclude other types of physical data associated with a subterraneanzone. In some aspects, this application provides a framework forprocessing large volumes of data, and the framework can be adapted forvarious applications that are not specifically described here. Forexample, the techniques and systems described here can be used toanalyze spatial coordinates, orientation data, or other types ofinformation collected from any source. As an example, soil or rocksamples can be collected (e.g., during drilling), and the concentrationof a given compound (e.g., a certain “salt”) as function of location canbe identified. This may help geophysicists and operators evaluate thegeo-layers in the ground.

FIG. 1A shows a schematic diagram of an example well system 100 with acomputing subsystem 110. The example well system 100 includes atreatment well 102 and an observation well 104. The observation well 104can be located remotely from the treatment well 102, near the treatmentwell 102, or at any suitable location. The well system 100 can includeone or more additional treatment wells, observation wells, or othertypes of wells. The computing subsystem 110 can include one or morecomputing devices or systems located at the treatment well 102, at theobservation well 104, or in other locations. The computing subsystem 110or any of its components can be located apart from the other componentsshown in FIG. 1A. For example, the computing subsystem 110 can belocated at a data processing center, a computing facility, or anothersuitable location. The well system 100 can include additional ordifferent features, and the features of the well system can be arrangedas shown in FIG. 1A or in any other suitable configuration.

The example treatment well 102 includes a well bore 101 in asubterranean zone 121 beneath the surface 106. The subterranean zone 121can include one or less than one rock formation, or the subterraneanzone 121 can include more than one rock formation. In the example shownin FIG. 1A, the subterranean zone 121 includes various subsurface layers122. The subsurface layers 122 can be defined by geological or otherproperties of the subterranean zone 121. For example, each of thesubsurface layers 122 can correspond to a particular lithology, aparticular fluid content, a particular stress or pressure profile, orany other suitable characteristic. In some instances, one or more of thesubsurface layers 122 can be a fluid reservoir that containshydrocarbons or other types of fluids. The subterranean zone 121 mayinclude any suitable rock formation. For example, one or more of thesubsurface layers 122 can include sandstone, carbonate materials, shale,coal, mudstone, granite, or other materials.

The example treatment well 102 includes an injection treatment subsystem120, which includes instrument trucks 116, pump trucks 114, and otherequipment. The injection treatment subsystem 120 can apply an injectiontreatment to the subterranean zone 121 through the well bore 101. Theinjection treatment can be a fracture treatment that fractures thesubterranean zone 121. For example, the injection treatment mayinitiate, propagate, or open fractures in one or more of the subsurfacelayers 122. A fracture treatment may include a mini fracture testtreatment, a regular or full fracture treatment, a follow-on fracturetreatment, a re-fracture treatment, a final fracture treatment oranother type of fracture treatment.

The fracture treatment can inject a treatment fluid into thesubterranean zone 121 at any suitable fluid pressures and fluid flowrates. Fluids can be injected above, at or below a fracture initiationpressure, above at or below a fracture closure pressure, or at anysuitable combination of these and other fluid pressures. The fractureinitiation pressure for a formation is the minimum fluid injectionpressure that can initiate or propagate artificial fractures in theformation. Application of a fracture treatment may or may not initiateor propagate artificial fractures in the formation. The fracture closurepressure for a formation is the minimum fluid injection pressure thatcan dilate existing fractures in the subterranean formation. Applicationof a fracture treatment may or may not dilate natural or artificialfractures in the formation.

A fracture treatment can be applied by any appropriate system, using anysuitable technique. The pump trucks 114 may include mobile vehicles,immobile installations, skids, hoses, tubes, fluid tanks or reservoirs,pumps, valves, or other suitable structures and equipment. In somecases, the pump trucks 114 are coupled to a working string disposed inthe well bore 101. During operation, the pump trucks 114 can pump fluidthrough the working string and into the subterranean zone 121. Thepumped fluid can include a pad, proppants, a flush fluid, additives, orother materials.

A fracture treatment can be applied at a single fluid injection locationor at multiple fluid injection locations in a subterranean zone, and thefluid may be injected over a single time period or over multipledifferent time periods. In some instances, a fracture treatment can usemultiple different fluid injection locations in a single well bore,multiple fluid injection locations in multiple different well bores, orany suitable combination. Moreover, the fracture treatment can injectfluid through any suitable type of well bore, such as, for example,vertical well bores, slant well bores, horizontal well bores, curvedwell bores, or any suitable combination of these and others.

A fracture treatment can be controlled by any appropriate system, usingany suitable technique. The instrument trucks 116 can include mobilevehicles, immobile installations, or other suitable structures. Theinstrument trucks 116 can include an injection control system thatmonitors and controls the fracture treatment applied by the injectiontreatment subsystem 120. In some implementations, the injection controlsystem can communicate with other equipment to monitor and control theinjection treatment. For example, the instrument trucks 116 maycommunicate with the pump truck 114, subsurface instruments, andmonitoring equipment.

The fracture treatment, as well as other activities and naturalphenomena, can generate microseismic events in the subterranean zone121, and microseismic data can be collected from the subterranean zone121. For example, the microseismic data can be collected by one or moresensors 112 associated with the observation well 104, or themicroseismic data can be collected by other types of systems. Themicroseismic information detected in the well system 100 can includeacoustic signals generated by natural phenomena, acoustic signalsassociated with a fracture treatment applied through the treatment well102, or other types of signals. For example, the sensors 112 may detectacoustic signals generated by rock slips, rock movements, rock fracturesor other events in the subterranean zone 121. In some instances, thelocations of individual microseismic events can be determined based onthe microseismic data.

Microseismic events in the subterranean zone 121 may occur, for example,along or near induced hydraulic fractures. The microseismic events maybe associated with pre-existing natural fractures or hydraulic fractureplanes induced by fracturing activities. In some environments, themajority of detectable microseismic events are associated withshear-slip rock fracturing. Such events may or may not correspond toinduced tensile hydraulic fractures that have significant widthgeneration. The orientation of a fracture can be influenced by thestress regime, the presence of fracture systems that were generated atvarious times in the past (e.g., under the same or a different stressorientation). In some environments, older fractures can be cemented shutover geologic time, and remain as planes of weakness in the rocks in thesubsurface.

The observation well 104 shown in FIG. 1A includes a well bore 111 in asubterranean region beneath the surface 106. The observation well 104includes sensors 112 and other equipment that can be used to detectmicroseismic information. The sensors 112 may include geophones or othertypes of listening equipment. The sensors 112 can be located at avariety of positions in the well system 100. In FIG. 1A, sensors 112 areinstalled at the surface 106 and beneath the surface 106 in the wellbore 111. Additionally or alternatively, sensors may be positioned inother locations above or below the surface 106, in other locationswithin the well bore 111, or within another well bore. The observationwell 104 may include additional equipment (e.g., working string,packers, casing, or other equipment) not shown in FIG. 1A. In someimplementations, microseismic data are detected by sensors installed inthe treatment well 102 or at the surface 106, without use of anobservation well.

In some cases, all or part of the computing subsystem 110 can becontained in a technical command center at the well site, in a real-timeoperations center at a remote location, in another appropriate location,or any suitable combination of these. The well system 100 and thecomputing subsystem 110 can include or access any suitable communicationinfrastructure. For example, well system 100 can include multipleseparate communication links or a network of interconnectedcommunication links. The communication links can include wired orwireless communications systems. For example, sensors 112 maycommunicate with the instrument trucks 116 or the computing subsystem110 through wired or wireless links or networks, or the instrumenttrucks 116 may communicate with the computing subsystem 110 throughwired or wireless links or networks. The communication links can includea public data network, a private data network, satellite links,dedicated communication channels, telecommunication links, or anysuitable combination of these and other communication links.

The computing subsystem 110 can analyze microseismic data collected inthe well system 100. For example, the computing subsystem 110 mayanalyze microseismic event data from a fracture treatment of asubterranean zone 121. Microseismic data from a fracture treatment caninclude data collected before, during, or after fluid injection. Thecomputing subsystem 110 can receive the microseismic data at anysuitable time. In some instances, the computing subsystem 110 receivesthe microseismic data in real time (or substantially in real time)during the fracture treatment. For example, the microseismic data may besent to the computing subsystem 110 immediately upon detection by thesensors 112. In some instances, the computing subsystem 110 receivessome or all of the microseismic data after the fracture treatment hasbeen completed. The computing subsystem 110 can receive the microseismicdata in any suitable format. For example, the computing subsystem 110can receive the microseismic data in a format produced by microseismicsensors or detectors, or the computing subsystem 110 can receive themicroseismic data after the microseismic data has been formatted,packaged, or otherwise processed. The computing subsystem 110 canreceive the microseismic data by any suitable means. For example, thecomputing subsystem 110 can receive the microseismic data by a wired orwireless communication link, by a wired or wireless network, or by oneor more disks or other tangible media.

The computing subsystem 110 can be used to perform fracture mapping inreal time during a fracture treatment. For example, the computingsubsystem 110 can receive microseismic data as a time series ofindividual microseismic events as the fracture treatment is applied. Atany given time, the computing subsystem 110 can identify fracture planesbased on the microseismic data that has been accumulated thus far. Whena new microseismic event is detected, the computing subsystem 110 canupdated the previously-generated fracture planes based on the newmicroseismic event. For example, the computing subsystem 110 canidentify a previously-generated fracture plane that is most likely to beassociated with the new microseismic event. The previously-generatedfracture plane can be identified, for example, based on spatialproximity or other considerations. The new microseismic event can becombined with other microseismic events associated with thepreviously-generated fracture plane, and the combined set ofmicroseismic events can be fitted to a plane. Various checks can beperformed, for example, to improve the accuracy of the results. In someinstances, the updated fracture plane can be displayed to a user in realtime, to allow the user to view the growth, propagation, or evolution offractures in the subterranean zone.

Some of the techniques and operations described herein may beimplemented by a computing subsystem configured to provide thefunctionality described. In various embodiments, a computing device mayinclude any of various types of devices, including, but not limited to,personal computer systems, desktop computers, laptops, notebooks,mainframe computer systems, handheld computers, workstations, tablets,application servers, storage devices, or any type of computing orelectronic device.

FIG. 1B is a diagram of the example computing subsystem 110 of FIG. 1A.The example computing subsystem 110 can be located at or near one ormore wells of the well system 100 or at a remote location. All or partof the computing subsystem 110 may operate independent of the wellsystem 100 or independent of any of the other components shown in FIG.1A. The example computing subsystem 110 includes a processor 160, amemory 150, and input/output controllers 170 communicably coupled by abus 165. The memory can include, for example, a random access memory(RAM), a storage device (e.g., a writable read-only memory (ROM) orothers), a hard disk, or another type of storage medium. The computingsubsystem 110 can be preprogrammed or it can be programmed (andreprogrammed) by loading a program from another source (e.g., from aCD-ROM, from another computer device through a data network, or inanother manner). The input/output controller 170 is coupled toinput/output devices (e.g., a monitor 175, a mouse, a keyboard, or otherinput/output devices) and to a communication link 180. The input/outputdevices receive and transmit data in analog or digital form overcommunication links such as a serial link, a wireless link (e.g.,infrared, radio frequency, or others), a parallel link, or another typeof link.

The communication link 180 can include any type of communicationchannel, connector, data communication network, or other link. Forexample, the communication link 180 can include a wireless or a wirednetwork, a Local Area Network (LAN), a Wide Area Network (WAN), aprivate network, a public network (such as the Internet), a WiFinetwork, a network that includes a satellite link, or another type ofdata communication network.

The memory 150 can store instructions (e.g., computer code) associatedwith an operating system, computer applications, and other resources.The memory 150 can also store application data and data objects that canbe interpreted by one or more applications or virtual machines runningon the computing subsystem 110. As shown in FIG. 1B, the example memory150 includes microseismic data 151, geological data 152, fracture data153, other data 155, and applications 156. In some implementations, amemory of a computing device includes additional or differentinformation.

The microseismic data 151 can include information on the locations ofmicroseisms in a subterranean zone. For example, the microseismic datacan include information based on acoustic data detected at theobservation well 104, at the surface 106, at the treatment well 102, orat other locations. The microseismic data 151 can include informationcollected by sensors 112. In some cases, the microseismic data 151 hasbeen combined with other data, reformatted, or otherwise processed. Themicroseismic event data may include any suitable information relating tomicroseismic events (locations, magnitudes, uncertainties, times, etc.).The microseismic event data can include data collected from one or morefracture treatments, which may include data collected before, during, orafter a fluid injection.

The geological data 152 can include information on the geologicalproperties of the subterranean zone 121. For example, the geologicaldata 152 may include information on the subsurface layers 122,information on the well bores 101, 111, or information on otherattributes of the subterranean zone 121. In some cases, the geologicaldata 152 includes information on the lithology, fluid content, stressprofile, pressure profile, spatial extent, or other attributes of one ormore rock formations in the subterranean zone. The geological data 152can include information collected from well logs, rock samples,outcroppings, microseismic imaging, or other data sources.

The fracture data 153 can include information on fracture planes in asubterranean zone. The fracture data 153 may identify the locations,sizes, shapes, and other properties of fractures in a model of asubterranean zone. The fracture data 153 can include information onnatural fractures, hydraulically-induced fractures, or any other type ofdiscontinuity in the subterranean zone 121. The fracture data 153 caninclude fracture planes calculated from the microseismic data 151. Foreach fracture plane, the fracture data 153 can include information(e.g., strike angle, dip angle, etc.) identifying an orientation of thefracture, information identifying a shape (e.g., curvature, aperture,etc.) of the fracture, information identifying boundaries of thefracture, or any other suitable information.

The applications 156 can include software applications, scripts,programs, functions, executables, or other modules that are interpretedor executed by the processor 160. Such applications may includemachine-readable instructions for performing one or more of theoperations represented in FIG. 4. The applications 156 may includemachine-readable instructions for generating a user interface or a plot,such as, for example, those represented in FIGS. 2A, 2B, 3A, 3B, 3C, 3D,3E, and 3F. The applications 156 can obtain input data, such asmicroseismic data, geological data, or other types of input data, fromthe memory 150, from another local source, or from one or more remotesources (e.g., via the communication link 180). The applications 156 cangenerate output data and store the output data in the memory 150, inanother local medium, or in one or more remote devices (e.g., by sendingthe output data via the communication link 180).

The processor 160 can execute instructions, for example, to generateoutput data based on data inputs. For example, the processor 160 can runthe applications 156 by executing or interpreting the software, scripts,programs, functions, executables, or other modules contained in theapplications 156. The processor 160 may perform one or more of theoperations represented in FIG. 4 or generate one or more of theinterfaces or plots shown in FIGS. 2A, 2B, 3A, 3B, 3C, 3D, 3E, and 3F.The input data received by the processor 160 or the output datagenerated by the processor 160 can include any of the microseismic data151, the geological data 152, the fracture data 153, or the other data155.

FIGS. 2A and 2B are plots showing example fracture planes. FIG. 2Aincludes a plot 200 a showing an initial fracture plane 208 a, anupdated fracture plane 208 b, and a microseismic event 206 a. The plot200 a shows the effect of updating the parameters of the initialfracture plane 208 a based on the new microseismic event 206 a. Inparticular, updating the parameters of the initial fracture plane 208 agenerates the updated fracture plane 208 b.

A fracture plane can be represented in any suitable coordinate system(e.g., spherical coordinates, rectangular coordinates, etc.). The plot200 a shows the fracture planes in a three-dimensional rectilinearcoordinate system. In the plot 200 a, the coordinate system isrepresented by the vertical axis 204 a and two horizontal axes 204 b and204 c. The vertical axis 204 a represents a range of depths in asubterranean zone; the horizontal axis 204b represents a range ofEast-West coordinates; and the horizontal axis 204 c represents a rangeof North-South coordinates (all in units of feet).

The initial fracture plane 208 a and the updated fracture plane 208 bare both represented by rectangular, two-dimensional bodies extendingthrough three-dimensional space. A fracture plane can have any othersuitable geometry, such as, for example, triangular, ellipsoidal,trapezoidal, an irregular geometry, or another type of geometry.

The plot 200 a shows one example of how the parameters of a fractureplane can be updated based on a single microseismic event. As shown bycomparing the two fracture planes in FIG. 2A, updating the initialfracture plane 208 a based on the microseismic event 206 a causes thefracture plane to grow in height and length; the updated fracture plane208 b has a greater vertical and horizontal extent than the initialfracture plane 208 a. Consequently, the updated fracture plane 208 b hasa larger area than the initial fracture plane 208 a. In some instances,updating a fracture plane changes the fracture plane in another manner.

FIG. 2B includes another plot 200 b showing an initial fracture plane208 c, an updated fracture plane 208 d, and a microseismic event 206 b.The plot 200 b shows the effect of updating the parameters of theinitial fracture plane 208 c based on the new microseismic event 206 b.In particular, updating the parameters of the initial fracture plane 208c generates the updated fracture plane 208 d.

The plot 200 b shows the fracture planes in a three-dimensionalrectilinear coordinate system represented by the vertical axis 204 d andtwo horizontal axes 204 e and 204 f. The axes in the plot 200 brepresent the same parameters as the axes in the plot 200 a, on adifferent scale. The initial fracture plane 208 c and the updatedfracture plane 208 d are both represented by rectangular,two-dimensional areas extending in the three-dimensional coordinatesystem.

As shown by comparing the two fracture planes in FIG. 2B, updating theinitial fracture plane 208 c based on the microseismic event 206 bcauses the fracture plane to rotate to a new orientation. For example,the updated fracture plane 208 d has a different orientation than theinitial fracture plane 208 c, with respect to the vertical andhorizontal axes in the plot 200 b. Accordingly, the updated fractureplane 208 d and the initial fracture plane 208 c define normal vectorshaving different orientations (i.e., pointing in non-parallel directionsin space).

FIGS. 3A-3F are plots showing updates for an example fracture plane. Theplots show an example time sequence for the fracture plane. FIG. 3Ashows a plot 300 a of an initial fracture plane 308 a; each subsequentplot in the time sequence shows the fracture plane as updated based on anew microseismic data point. FIG. 3B shows a plot 300 b of a firstupdated fracture plane 308 b; FIG. 3C shows a plot 300 c of a secondupdated fracture plane 308 c; FIG. 3D shows a plot 300 d of a thirdupdated fracture plane 308 d; FIG. 3E shows a plot 300 e of a fourthupdated fracture plane 308 e; and FIG. 3F shows a plot 300 f of a fifthupdated fracture plane 308 f. In each plot, the previous version of thefracture plane is shown for comparison. The plots in FIGS. 3A-3F alsoshow the well bore 310 and microseismic events 306.

Each of the plots 300 a, 300 b, 300 c, 300 d, 300 e, and 300 f shows therespective fracture planes in a three-dimensional rectilinear coordinatesystem represented by the vertical axis 304 a and two horizontal axes304 b and 304 c. The vertical axis 304 a represents a range of depths ina subterranean zone; the horizontal axis 304 b represents a range ofEast-West coordinates; and the horizontal axis 304 c represents a rangeof North-South coordinates (all in units of feet). As shown in thefigures, the axes are scaled for each respective plot. In the examplesshown in FIGS. 3A-3F, the fracture planes are represented bytwo-dimensional, rectangular areas extending in the three-dimensionalcoordinate system. Fracture planes can have other spatial geometries.

The initial fracture plane 308 a and the updated fracture planes 308 b,308 c, 308 d, 308 e, and 308 f represent the growth and evolution of anindividual fracture over time. In the example shown, the initialfracture plane 308 a is identified when the 40^(th) microseismic eventis received; the 87^(th) microseismic event triggers an updatealgorithm. For example, the process 430 shown in FIG. 4 (or anotherprocess) can be used to update a fracture plane based on a newmicroseismic event. FIG. 3B shows that updating the fracture plane basedon the 87^(th) microseismic event changes the fracture plane'sorientation. In particular, updating the initial fracture plane 308 abased on the 87^(th) microseismic event causes the fracture plane torotate to a new orientation, and the first updated fracture plane 308 bhas a different orientation than the initial fracture plane 308 a. Theremaining updates shown in FIGS. 3C-3F cause the fracture plane topropagate, and the plots show how the fracture plane's area increases astime progresses.

FIG. 3C shows an update based on the 89^(th) microseismic eventreceived. Updating the first updated fracture plane 308 b based on the89^(th) microseismic event causes the fracture plane to grow vertically,and the second updated fracture plane 308 c is taller than the firstupdated fracture plane 308 b. FIG. 3D shows an update based on the130^(th) microseismic event received. Updating the second updatedfracture plane 308 c based on the 130^(th) microseismic event causes thefracture plane to grow vertically, and the third updated fracture plane308 d is taller than the second updated fracture plane 308 c. FIG. 3Eshows an update based on the 152^(nd) microseismic event received.Updating the third updated fracture plane 308 d based on the 152^(nd)microseismic event causes the fracture plane to grow horizontally(toward the left in the figure), and the fourth updated fracture plane308 e is longer than the third updated fracture plane 308 d. FIG. 3Fshows an update based on the 157^(th) microseismic event received.Updating the third updated fracture plane 308 d based on the 157^(th)microseismic event causes the fracture plane to grow horizontally(toward the right in the figure) and vertically, and the fifth updatedfracture plane 308 f is longer and taller than the fourth updatedfracture plane 308 e.

FIG. 4 is a flow chart of an example process 430 for analyzingmicroseismic data. Some or all of the operations in the process 430 canbe implemented by one or more computing devices. In someimplementations, the process 430 may include additional, fewer, ordifferent operations performed in the same or a different order.Moreover, one or more of the individual operations or subsets of theoperations in the process 430 can be performed in isolation or in othercontexts. Output data generated by the process 430, including outputgenerated by intermediate operations, can include stored, displayed,printed, transmitted, communicated or processed information.

In some implementations, some or all of the operations in the process430 are executed in real time during a fracture treatment. An operationcan be performed in real time, for example, by performing the operationin response to receiving data (e.g., from a sensor or monitoring system)without substantial delay. An operation can be performed in real time,for example, by performing the operation while monitoring for additionalmicroseismic data from the fracture treatment. Some real time operationscan receive an input and produce an output during a fracture treatment;in some instances, the output is made available to a user within a timeframe that allows an operator to respond to the output, for example, bymodifying the fracture treatment.

In some cases, some or all of the operations in the process 430 areexecuted dynamically during a fracture treatment. An operation can beexecuted dynamically, for example, by iteratively or repeatedlyperforming the operation based on additional inputs, for example, as theinputs are made available. In some instances, dynamic operations areperformed in response to receiving data for a new microseismic event (orin response to receiving data for a certain number of new microseismicevents, etc.).

At 400, microseismic data for a new microseismic event are received. Forexample, the microseismic data can be obtained by reading themicroseismic data from memory, by receiving the microseismic data from aremote device, or in a different manner. The microseismic data mayinclude information on the measured location of the new microseismicevent, information on a measured magnitude of the new microseismicevent, information on an uncertainty associated with the newmicroseismic event, or information on a time associated with the newmicroseismic event, etc. The microseismic data are collected from afracture treatment. For example, the microseismic event data may includemicroseismic data collected at an observation well, at a treatment well,at the surface, or at other locations in a well system. Microseismicdata from a fracture treatment can include data for microseismic eventsdetected before, during, or after the fracture treatment is applied. Forexample, in some instances, microseismic monitoring begins before thefracture treatment is applied, ends after the fracture treatment isapplied, or both.

At 401, a previously-generated fracture plane is selected. In thisexample, the fracture plane is “previously-generated” in the sense thatit was generated before the data for the new microseismic event wasreceived. In some implementations, parameters of a previously-generatedfracture plane are the parameters that were identified from microseismicdata collected before the new microseismic event was detected. The priormicroseismic event data and the new microseismic event can be part of amicroseismic data set from the same fracture treatment of a subterraneanzone. In some instance, the prior microseismic event data and the newmicroseismic event are from different fracture treatments.

Fracture planes (e.g., the previously-generated fracture plane selectedat 401) can be identified from microseismic data by any suitableoperation, process or algorithm. A fracture plane can be identified bycomputing the parameters of the fracture plane, for example, from thelocations and other parameters of the measured microseismic events. Insome cases, the fracture planes are identified in real time during thefracture treatment. Example techniques for identifying fracture planesfrom microseismic data are described in U.S. Provisional ApplicationSer. No. 61/710,582, filed on Oct. 5, 2012.

In some instances, when the data are received at 400, several fractureplanes have already been generated. For example, tens or hundreds offracture planes may have already been identified frompreviously-received microseismic data. As such, in some cases, aparticular fracture plane is selected from multiple previously-generatedfracture planes at 401. For example, the particular fracture plane canbe selected from a list of previously-generated fracture planes based onan index, selection criteria, or other information.

At 402, the distance between the new microseismic event and the selectedfracture plane is calculated. The distance can be calculated, forexample, based on the spatial coordinates of the new microseismic eventand the parameters of the selected fracture plane. In some instance, thedistance calculation can account for uncertainty in the location of themicroseismic event, uncertainty in the location of the fracture plane,or both. Other information can be accounted for in calculating thedistance.

At 403, the distance between the new microseismic event and the selectedfracture plane is compared to a control parameter. The control parametercan be a threshold value for determining whether the selected fractureplane is close enough to the new microseismic event, for example, toconsider the new microseismic event as a supporting event for theselected fracture plane. The control parameter can be apreviously-designated threshold value (e.g., a system constant). Thecontrol parameter can be a dynamically computed value. For example, thecontrol parameter can be computed based on the parameters of theselected fracture plane, parameters of other previously-generatedfracture planes, or based on other information.

If the distance between the new microseismic event and the selectedfracture plane is not less than the control parameter, the process 430progresses to operation 450. At 450, if there are otherpreviously-generated fracture planes that have not been selected, theprocess 430 progresses to operation 401. Accordingly, in some cases, theoperations 400, 402, 403, 450, and 401 cause the process 430 tosequentially select multiple different previously-generated fractureplanes.

The process 430 can use any suitable algorithm or technique tosystematically progress through previously-generated fracture planes.For example, an indexed list of the previously-generated fracture planescan be created. The list can be sorted, for example, based on size,confidence, time, or other parameters of the fracture planes, or thelist can be unsorted. A stored index can be used to systematicallyselect a different previously-generated fracture plane from the listeach time operation 401 is executed.

In some cases, the previously-generated fracture planes are selected insequence until all the previously-generated fracture planes have beenselected. At 450, if all the previously-generated fracture planes havebeen selected, the new microseismic event is designated as anunassociated event at 460. As such, the new microseismic event can belabeled, tagged, or otherwise designated as not supporting any fractureplane.

In some cases, the previously-generated fracture planes are selected insequence until the distance between a selected fracture plane and thenew microseismic event is less than the control parameter. At 403, ifthe distance between the new microseismic event and the selectedfracture plane is not less than the control parameter, the process 430progresses to operation 415. At 415, the selected fracture plane isupdated based on the new microseismic event. The operations in thedashed box in FIG. 4 represent an example technique for updating afracture plane based on a new microseismic event. Other techniques canbe used.

At 404, parameters of the selected fracture plane are computed. In theexample shown, the orientation, area, and distance residual of thefracture plane are calculated. The area of the fracture plane indicatesthe fracture plane's two-dimensional size. The distance residual of afracture plane indicates the average distance between the fracture planeand the fracture plane's supporting events.

The orientation of the fracture plane indicates the fracture plane'sangle, for example, in a specified coordinate system. The orientationcan be specified, for example, by particular values of the dip angle andstrike angle. New strike and dip values can be calculated at 404 using aChi-square fitting technique or other techniques. In some cases, thechange in orientation is small, for example, due to the conditionalcontrol of the distance.

The parameters of the selected fracture plane can be calculated based onthe new microseismic event and other microseismic events. For example,the other microseismic events can be prior microseismic events thatoccurred or were detected (or both) before the new microseismic event.In some cases, the other microseismic events are the set of supportingmicroseismic events that were used to compute the previously-generatedfracture plane (i.e., the fracture plane that was selected at 401).

At 405, the distance residual is compared to a tolerance value. Thetolerance value can be a threshold value for determining whether themicroseismic events that were used to update the fracture plane are (onaverage) close enough to the updated fracture plane, for example, toconsider the new microseismic event as a supporting event for theupdated fracture plane. The tolerance value can be apreviously-designated threshold value (e.g., a system constant). Thetolerance value can be a dynamically computed value.

At 405, if the distance residual is less than the control parameter, theprocess 430 progresses to operation 410. At 410, the updated fractureplane can be stored, and the new microseismic event is associated withthe updated fracture plane.

At 405, if the distance residual is not less than the control parameter,the process 430 progresses to operation 406. At 406, the distanceresidual can be reduced by disassociating one or more microseismicevents from the selected fracture plane. For example, one or moremicroseismic events that are the greatest distance from the updatedversion of the selected fracture plane can be disassociated from thefracture plane.

At 407, new fracture plane parameters are calculated from themicroseismic events remaining after one or more microseismic events weredisassociated at 406. If the distance residual has not been reduced orif the distance residual has not been reduced by an acceptable amount(e.g., less than the tolerance value or some other threshold), then theprocess 430 progresses to operation 460. At 460, the new microseismicevent is designated as an unassociated event at 460. As such, themicroseismic event can be labeled, tagged, or otherwise designated asnot supporting any fracture plane. In some instances, the selectedfracture plane can be restored to its previously-generated parameters.In other words, if the new microseismic event is designated asunassociated, the updated parameters for the fracture plane calculatedat 404 can be discarded.

In some cases, the process 430 improves or optimizes the distanceresidual and area when the selected fracture plane is updated at 415.For example, when the updated fracture plane's distance residual is lessthan the tolerance value at 405, the new microseismic event becomesassociated with the fracture plane at 410. Otherwise, other microseismicevents in the supporting set can be disassociated until the distanceresidual falls within the threshold value. In some cases, the change ofthe fracture plane's orientation or size causes some microseismic eventsto be disassociated from the fracture plane. In some cases, the changeof the fracture plane's orientation or size causes some microseismicevents to be associated to the fracture plane. In some instances, amicroseismic event can be associated with multiple fracture planes. Theassociation of a microseismic event with multiple fracture planes canindicate that the fracture planes intersect.

If the distance residual has been reduced by an acceptable amount at407, then the process 430 progresses to operation 408. At 408, the areaof the updated fracture plane (after disassociating one or moremicroseismic events at 406) is computed. The area can be the size of thefracture plane generated from the microseismic events still associatedwith the fracture plane after the events were disassociated at 406. Ifthe size of the fracture plane is not greater than the prior area of thefracture plane, then the process 430 progresses to operation 460(described above). As such, the check performed at 408 can ensure thatassociating the new microseismic event to the fracture plane does notcause the fracture plane to shrink. This check can be performed, forexample, to incorporate physical or geological constraints in a fracturematching algorithm. For example, the check can be performed to reflectknowledge that fractures tend to grow (rather than shrink) during afracture treatment. Experience shows that new microseismic events aretypically associated with a fracture propagating in length or growing inheight. As such, the non-decreasing area condition can be imposed toensure that the an updated fracture plane's area is larger than or equalto that of the original plane. Other assumptions are used in someenvironments.

At 408, if the size of the fracture plane is greater than the prior areaof the fracture plane, then the process 430 progresses to operation 410.At 410, the new microseismic event is associated with the fractureplane. If any microseismic events were disassociated at 406, thosemicroseismic events can be designated as unassociated, or they may behandled in a different manner. Example techniques for handlingdisassociated microseismic events are described in U.S. ProvisionalApplication Ser. No. 61/710,582, filed on Oct. 5, 2012. In someinstances, the updated fracture plane parameters (i.e., the fractureplane parameters based on the microseismic events that remain associatedafter 406) are stored as the updated fracture plane.

The example process 430 includes checks that can improve the accuracy ofa fracture matching algorithm. For example, some or all of thecomparisons at 403, 405, 407, and 408 can help to improve confidencethat the updated fracture plane corresponds to a physical fracture inthe subterranean zone. The comparisons can be adjusted for a particularenvironment, as appropriate. In some cases, additional or differentcomparisons can be made. For example, in some cases, an accuracyconfidence value is used to determine whether to associated a newmicroseismic event to a plane. Example techniques for calculating anaccuracy confidence value for a fracture plane are described in U.S.Provisional Application Ser. No. 61/710,582, filed on Oct. 5, 2012.

In some implementations, a graphical representation of the updatedfracture planes is generated. The graphical representation can bedisplayed, for example, to present the updated fracture plane in realtime during the fracture treatment. The graphical representation caninclude a single fracture plane or multiple fracture planes. Thegraphical representation can include a three-dimensional representationof the fracture plane, a three-dimensional representation of themicroseismic events associated with the fracture plane, or a combinationof these and other features. Examples of a graphical representation of afracture plane are shown in FIGS. 2A, 2B, 3A, 3B, 3C, 3D, 3E, and 3F.Other types of graphical representations can be used.

The graphical representation can be displayed on a monitor, screen, orother type of display device. In some instances, the display is updated.For example, the displayed graphical representation can be updated basedon additional microseismic event data from the fracture treatment.Displaying (and in some cases, updating) the graphical representationcan allow a user to view dynamic behavior associated with a fracturetreatment. In some cases, a fracture plane can be updated as additionalmicroseismic data is accumulated, and the updates may cause the fractureplane to grow or change orientation.

Some embodiments of subject matter and operations described in thisspecification can be implemented in digital electronic circuitry, or incomputer software, firmware, or hardware, including the structuresdisclosed in this specification and their structural equivalents, or incombinations of one or more of them. Some embodiments of subject matterdescribed in this specification can be implemented as one or morecomputer programs, i.e., one or more modules of computer programinstructions, encoded on computer storage medium for execution by, or tocontrol the operation of, data processing apparatus. A computer storagemedium can be, or can be included in, a computer-readable storagedevice, a computer-readable storage substrate, a random or serial accessmemory array or device, or a combination of one or more of them.Moreover, while a computer storage medium is not a propagated signal, acomputer storage medium can be a source or destination of computerprogram instructions encoded in an artificially generated propagatedsignal. The computer storage medium can also be, or be included in, oneor more separate physical components or media (e.g., multiple CDs,disks, or other storage devices).

The term “data processing apparatus” encompasses all kinds of apparatus,devices, and machines for processing data, including by way of example aprogrammable processor, a computer, a system on a chip, or multipleones, or combinations, of the foregoing. The apparatus can includespecial purpose logic circuitry, e.g., an FPGA (field programmable gatearray) or an ASIC (application specific integrated circuit). Theapparatus can also include, in addition to hardware, code that createsan execution environment for the computer program in question, e.g.,code that constitutes processor firmware, a protocol stack, a databasemanagement system, an operating system, a cross-platform runtimeenvironment, a virtual machine, or a combination of one or more of them.The apparatus and execution environment can realize various differentcomputing model infrastructures, such as web services, distributedcomputing and grid computing infrastructures.

A computer program (also known as a program, software, softwareapplication, script, or code) can be written in any form of programminglanguage, including compiled or interpreted languages, declarative orprocedural languages. A computer program may, but need not, correspondto a file in a file system. A program can be stored in a portion of afile that holds other programs or data (e.g., one or more scripts storedin a markup language document), in a single file dedicated to theprogram in question, or in multiple coordinated files (e.g., files thatstore one or more modules, sub programs, or portions of code). Acomputer program can be deployed to be executed on one computer or onmultiple computers that are located at one site or distributed acrossmultiple sites and interconnected by a communication network.

Some of the processes and logic flows described in this specificationcan be performed by one or more programmable processors executing one ormore computer programs to perform actions by operating on input data andgenerating output. The processes and logic flows can also be performedby, and apparatus can also be implemented as, special purpose logiccircuitry, e.g., an FPGA (field programmable gate array) or an ASIC(application specific integrated circuit).

Processors suitable for the execution of a computer program include, byway of example, both general and special purpose microprocessors, andprocessors of any kind of digital computer. Generally, a processor willreceive instructions and data from a read only memory or a random accessmemory or both. A computer includes a processor for performing actionsin accordance with instructions and one or more memory devices forstoring instructions and data. A computer may also include, or beoperatively coupled to receive data from or transfer data to, or both,one or more mass storage devices for storing data, e.g., magnetic,magneto optical disks, or optical disks. However, a computer need nothave such devices. Devices suitable for storing computer programinstructions and data include all forms of non-volatile memory, mediaand memory devices, including by way of example semiconductor memorydevices (e.g., EPROM, EEPROM, flash memory devices, and others),magnetic disks (e.g., internal hard disks, removable disks, and others),magneto optical disks , and CD ROM and DVD-ROM disks. The processor andthe memory can be supplemented by, or incorporated in, special purposelogic circuitry.

To provide for interaction with a user, operations can be implemented ona computer having a display device (e.g., a monitor, or another type ofdisplay device) for displaying information to the user and a keyboardand a pointing device (e.g., a mouse, a trackball, a tablet, a touchsensitive screen, or another type of pointing device) by which the usercan provide input to the computer. Other kinds of devices can be used toprovide for interaction with a user as well; for example, feedbackprovided to the user can be any form of sensory feedback, e.g., visualfeedback, auditory feedback, or tactile feedback; and input from theuser can be received in any form, including acoustic, speech, or tactileinput. In addition, a computer can interact with a user by sendingdocuments to and receiving documents from a device that is used by theuser; for example, by sending web pages to a web browser on a user'sclient device in response to requests received from the web browser.

A client and server are generally remote from each other and typicallyinteract through a communication network. Examples of communicationnetworks include a local area network (“LAN”) and a wide area network(“WAN”), an inter-network (e.g., the Internet), a network comprising asatellite link, and peer-to-peer networks (e.g., ad hoc peer-to-peernetworks). The relationship of client and server arises by virtue ofcomputer programs running on the respective computers and having aclient-server relationship to each other.

In some aspects of what is described here, dominant orientationsembedded in sets of fractures associated with microseismic events can bedynamically identified during a fracture treatment. For example,fracture planes can be extracted from real time microseismic eventscollected from the field. The fracture planes can be identified based onmicroseismic event information including: event locations, eventlocation measurement uncertainties, event moment magnitudes, eventoccurrence times, and others. At each point in time, data can beassociated with previously-computed basic planes, including themicroseismic supporting set of events.

In some aspects of what is described here, a probability histogram ordistribution of basic planes can be constructed from the microseismicevents collected, and the histogram or distribution can be used forderiving the dominant fracture orientations. Fractures extracted alongthe dominant orientations can, in some instances, provide an optimalmatch to the real time microseismic events. The histogram ordistribution and the dominant orientations can have non-negligiblesensitivity to the new incoming microseismic event. As such, some planesidentified during the time microseismic data are assimilated may not beaccurate when comparing to the post microseismic event data results.Example techniques for generating, updating, and using histograms basedon microseismic data are described in U.S. Provisional Application Ser.No. 61/710,582, filed on Oct. 5, 2012.

In some aspects of what is described here, an accuracy confidenceparameter can provide a measure for the accuracy of real-time identifiedplanes. Factors impacting a plane's accuracy confidence can include anevent's intrinsic properties, the relationship between support eventsand the plane, and the weight reflecting the fracture orientation trendsof post microseismic event data. In some instances, fracture planes withhigh confidence at the end of hydraulic fracturing treatment that wereidentified in real time fashion are consistent with those obtained fromthe post event data.

In some aspects, some or all of the features described here can becombined or implemented separately in one or more software programs forreal-time automated fracture mapping. The software can be implemented asa computer program product, an installed application, a client-serverapplication, an Internet application, or any other suitable type ofsoftware. In some cases, a real-time automated fracture mapping programcan dynamically show users spatial and temporal evolution of identifiedfracture planes in real-time as microseismic events graduallyaccumulate. The dynamics may include, for example, the generation of newfractures, the propagation and growth of existing fractures, or otherdynamics. In some cases, a real-time automated fracture mapping programcan provide users the ability to view the real-time identified fractureplanes in multiple confidence levels. In some instances, users mayobserve spatial and temporal evolution of the high confidence levelfractures, which may exhibit the dominant trends of overall microseismicevent data. In some cases, a real-time automated fracture mappingprogram can evaluate fracture accuracy confidence, for example, tomeasure the certainty of identified fracture planes. The accuracyconfidence values may, for example, help users better understand andanalyze changes in a probability histogram or orientation distribution,which may continuously vary with the real-time accumulation ofmicroseismic events. In some cases, a real-time automated fracturemapping program can provide results that are consistent with post datafracture mapping. For example, at the end of the hydraulic fracturetreatment, the results produced by the real-time automated fracturemapping program can be statistically consistent with those obtained by apost data automated fracture mapping program operating on the same data.Such features may allow field engineers, operators and analysts, todynamically visualize and monitor spatial and temporal evolution ofhydraulic fractures, to analyze the fracture complexity and reservoirgeometry, to evaluate the effectiveness of hydraulic fracturingtreatment and to improve the well performance.

While this specification contains many details, these should not beconstrued as limitations on the scope of what may be claimed, but ratheras descriptions of features specific to particular examples. Certainfeatures that are described in this specification in the context ofseparate implementations can also be combined. Conversely, variousfeatures that are described in the context of a single implementationcan also be implemented in multiple embodiments separately or in anysuitable subcombination.

A number of embodiments have been described. Nevertheless, it will beunderstood that various modifications can be made. Accordingly, otherembodiments are within the scope of the following claims.

1. A computer-implemented method for analyzing microseismic data from afracture treatment, the method comprising: receiving data for a newmicroseismic event associated with a fracture treatment of asubterranean zone; calculating, by data processing apparatus, an updatedparameter for a fracture plane, the fracture plane beingpreviously-generated based on data for prior microseismic events, theupdated parameter calculated based on the data for the new microseismicevent and the data for the prior microseismic events; and displaying agraphical representation of the fracture plane based on the updatedparameter.
 2. The method of claim 1, wherein the updated parameter iscalculated and the graphical representation is displayed in real timeduring the fracture treatment.
 3. The method of claim 1, furthercomprising: selecting the fracture plane, from a plurality of fractureplanes, based on the data for the new microseismic event; andassociating the new microseismic event with the selected fracture plane.4. The method of claim 3, wherein displaying a graphical representationof the fracture plane includes updating a graphical representation ofthe plurality of fracture planes in real time during the fracturetreatment.
 5. The method of claim 3, wherein selecting the fractureplane from a plurality of fracture planes includes: determining adistance between the new microseismic event and the facture plane; anddetermining that the distance is less than a threshold distance.
 6. Themethod of claim 5, further comprising computing the threshold distanceby multiplying a predefined coefficient by the standard deviation of themicroseismic events associated with the fracture plane.
 7. The method ofclaim 1, wherein calculating an updated parameter for the fracture planeincludes calculating at least one of an updated orientation or anupdated area for the fracture plane based on the data for the newmicroseismic event and the data for the prior microseismic events. 8.The method of claim 1, wherein calculating an updated parameter for thefracture plane includes calculating an average distance from thefracture plane for the new microseismic event and the prior microseismicevents.
 9. The method of claim 8, wherein the new microseismic event andthe prior microseismic events define a set, and the method furthercomprises: detecting that the average distance is greater than apredefined threshold distance; and calculating an updated averagedistance after removing one or more microseismic events from the set.10. The method of claim 1, wherein calculating an updated parameter forthe fracture plane includes calculating an updated area for the fractureplane, and the method further comprises: comparing the updated area forthe fracture plane to a prior area for the fracture plane; anddisassociating the new microseismic event from the fracture plane if theupdated area for the fracture plane is less than the prior area for thefracture plane.
 11. The method of claim 1, further comprising:identifying one or more microseismic events that are farther than athreshold distance from the fracture plane; and disassociating theidentified microseismic events from the fracture plane.
 12. The methodof claim 1, wherein the new microseismic event comprises a first newmicroseismic event, the method further comprising: after displaying thegraphical representation based on the first new microseismic event,receiving data for a second new microseismic event from the fracturetreatment; calculating a second updated parameter for the fracture planebased in part on the data for the second new microseismic event; anddisplaying a graphical representation of the fracture plane based on thesecond updated parameter.
 13. A non-transitory computer-readable mediumencoded with instructions that, when executed by data processingapparatus, perform operations comprising: receiving data for a newmicroseismic event associated with a fracture treatment of asubterranean zone; calculating an updated parameter for a fractureplane, the fracture plane being previously generated based on data forprior microseismic events, the updated parameter calculated based on thedata for the new microseismic event and the data for the priormicroseismic events; and generating a graphical representation of thefracture plane based on the updated parameter.
 14. The computer-readablemedium of claim 13, wherein the updated parameter is calculated and thegraphical representation is generated in real time during the fracturetreatment.
 15. The computer-readable medium of claim 13, the operationsfurther comprising: selecting the fracture plane, from a plurality offracture planes, based on the data for the new microseismic event; andassociating the new microseismic event with the selected fracture plane.16. The computer-readable medium of claim 13, wherein calculating anupdated parameter for the fracture plane includes calculating at leastone of an updated orientation or an updated area for the fracture planebased on the data for the new microseismic event and the data for theprior microseismic events.
 17. The computer-readable medium of claim 13,wherein calculating an updated parameter for the fracture plane includescalculating an average distance from the fracture plane for the newmicroseismic event and the prior microseismic events.
 18. A systemcomprising: data processing apparatus operable to: receive data for anew microseismic event associated with a fracture treatment of asubterranean zone; and calculate an updated parameter for a fractureplane, the fracture plane being previously generated based on data forprior microseismic events, the updated parameter calculated based on thedata for the new microseismic event and the data for the priormicroseismic events; and a display device operable to display agraphical representation of the fracture plane based on the updatedparameter.
 19. The system of claim 18, further comprising acommunication interface operable to receive microseismic event data fromone or more sensors associated with the subterranean zone.
 20. Thesystem of claim 18, wherein the updated parameter is calculated and thegraphical representation is displayed in real time during the fracturetreatment.
 21. The system of claim 18, wherein the data processingapparatus is further operable to: select the fracture plane, from aplurality of fracture planes, based on the data for the new microseismicevent; and associate the new microseismic event with the selectedfracture plane.
 22. The system of claim 18, wherein calculating anupdated parameter for the fracture plane includes calculating at leastone of an updated orientation or an updated area for the fracture planebased on the data for the new microseismic event and the data for theprior microseismic events.